Cancer Cell Behavior Prediction Software Advances Treatment
Revolutionizing Cancer Research: New Computational Framework Simulates Cellular Ecosystems for Precision Oncology
Baltimore, MD – Scientists have developed a groundbreaking computational framework that allows researchers to build virtual models of cellular ecosystems, paving the way for more precise and personalized cancer treatments. This innovative approach, detailed in a recent study, leverages genomics data to simulate the complex interactions within tumors, offering unprecedented insights into how individual patients might respond to therapies like immunotherapy.The research, spearheaded by the University of Maryland School of Medicine (UMSOM), utilizes a novel ”computational grammar” to create dynamic, predictive models of biological systems. By analyzing genomics data from untreated pancreatic cancer tissue samples, the team was able to generate virtual “patients” whose predicted responses to immunotherapy varied significantly. This highlights the critical role of the entire cellular environment, or “ecosystem,” in determining treatment efficacy – a key tenet of precision oncology.
Pancreatic cancer, notoriously challenging to treat, is often characterized by a dense network of non-cancerous cells called fibroblasts surrounding the tumor.The researchers employed advanced spatial genomics technology to visualize and understand how these fibroblasts communicate with cancer cells. This allowed them to track the growth and spread of pancreatic tumors from real patient tissue within their virtual models.
“What makes these models so exciting to me as someone who studies immunology is that they can be informed, initialized, and built upon using both laboratory and human genomics data,” explained Dr. Johnson, a researcher involved in the study. “Immune cells are amazing and follow rules of behavior that can be programmed into one of these models. So, as an example, we can take data and treat it as a snapshot of what the human immune system is doing, and this framework gives us a sandbox to freely investigate our hypotheses of what’s happening there over time without extra costs or risk to patients.”
Elana J. Fertig, PhD, Director of the Institute for Genome Sciences (IGS) at UMSOM and a lead author on the study, drew parallels to her previous work in weather prediction. “Ever as my transitioning from my training in weather prediction at the university of Maryland, College Park into computation, I have believed that we coudl apply the same principles to work across biological systems to make predictive models in cancer,” she stated. “I am struck by how many rules of biology we don’t yet know. Adapting this approach to genomics technologies gives us a virtual cell laboratory in which we can conduct experiments to test the implications of cellular rules entirely in silico.”
This “tapestry of team science,” as Dr. Fertig described it,received validation from clinical collaborators at Johns Hopkins University and Oregon Health Sciences University,with funding provided by the National Foundation for Cancer research.
The new computational grammar is open-source, ensuring its accessibility to the global scientific community. “By making this tool accessible to the scientific community, we are providing a path forward to standardize such models and make them generally accepted,” said Dr. Bergman. To showcase its broad applicability, researchers led by Genevieve stein-O’Brien, PhD, of Johns Hopkins School of Medicine, successfully applied the framework to simulate brain development, modeling the creation of neural layers.Mark T. Gladwin, MD, Vice President for Medical Affairs at the University of Maryland, Baltimore, and Dean of UMSOM, emphasized the transformative potential of this work. “With this work from IGS, we have a new framework for biological research as researchers can now create computerized simulations of their bench experiments and clinical trials and even start predicting the effects of therapies on patients,” he said. “This has important applications to enable digital twins and virtual clinical trials in cancer and beyond. We look forward to future work extending this computational modeling of cancer to the clinic.”
This pioneering computational framework promises to accelerate the development of personalized cancer therapies by providing a powerful tool for understanding and predicting treatment responses within the complex biological landscape of each patient.